HyperAgent: Planning and Acting over Tool-Schema Hypergraphs for Tool-Use LLM Agents
Quick Answer
HyperAgent introduces a Tool-Schema Hypergraph framework that enhances LLM agents' tool-use planning and execution.
Quick Take
By dynamically constructing a schema-aware Task DAG and a state-conditioned tool support graph, it significantly improves task completion performance in AppWorld while reducing redundant API calls and token consumption compared to existing baselines.
Key Points
- HyperAgent models tool relations using a directed Tool-Schema Hypergraph.
- It constructs a schema-aware Task DAG for efficient task execution.
- The framework reduces redundant API calls and interactions.
- Experiments show improved task completion performance in AppWorld.
- Dynamic planning addresses unresolved requirements during execution.
DeepSignal Analysis
What happened
HyperAgent introduces a Tool-Schema Hypergraph framework designed to enhance the planning and execution capabilities of large language model (LLM) agents when using external tools. This framework allows for the dynamic construction of a task-aware directed acyclic graph (DAG) and a tool support graph, which collectively improve task completion rates in the AppWorld environment.
Key evidence
- HyperAgent constructs a directed Tool-Schema Hypergraph, representing tools as hyperedges connecting input and output schema nodes.
- The framework dynamically creates a schema-aware Task DAG and a state-conditioned tool support graph to guide execution.
- Experiments in AppWorld show that HyperAgent reduces redundant API calls and token consumption while improving task completion performance compared to existing baselines.
Why it matters
The development of HyperAgent addresses significant challenges in tool-use planning for LLM agents, particularly the inefficiencies associated with implicit reasoning and the complexities of real-world environments. By modeling tool relations at the schema level, it enhances the reliability and efficiency of task execution, which is crucial for the practical deployment of LLMs in various applications.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains challenging due to the limitations of implicit reasoning and the evolving nature of real-world execution environments. Existing tool-use agents typically rely on LLMs to infer tool compositions from textual descriptions, which can lead to inefficient exploration and unreliable execution in complex tasks. To address these challenges, we model tool relations at the schema level and construct a directed Tool--Schema Hypergraph, in which tools are represented as hyperedges from their required input-schema nodes to their output-schema nodes. Furthermore, we propose HyperAgent, a Tool--Schema Hypergraph-guided framework for dynamic planning and execution. Given a task, HyperAgent first extracts a task-relevant tool context graph and uses it to guide the construction of a schema-aware Task DAG. During execution, HyperAgent dynamically realizes each subtask by constructing a state-conditioned tool support graph through deficit-oriented expansion, which identifies unresolved requirements and retrieves supporting producer tools according to the current agent state. Experiments on AppWorld demonstrate that HyperAgent improves task completion performance while reducing redundant API calls, LLM interactions, and token consumption compared with existing agent baselines.
| Subjects: | Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2608.02650 [cs.AI] |
| (or arXiv:2608.02650v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02650 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zian Zhai [view email]
[v1]
Fri, 31 Jul 2026 21:42:35 UTC (1,090 KB)
— Originally published at arxiv.org
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